The above phenomenon shows a fact, which has never been discussed in recent researches, that different graphs may have different preferences for node and edge features. To those graphs that edges possess a great impact, it is infelicitous to treat edge features as weights or labels. However, all existing works have ignored such a key fact. Our work's motivation is not only to integrate edge features into GATs but also to propose general models that spontaneously handle such preferences. To our best knowledge, we are the first to try to solve such problems. It should be emphasized that we do not want to present disparate models against state-of-the-art approaches. Since the attention mechanism has proved itself in lots of tasks, it is unnecessary to propose a completely new one. Our work is an extension of GATs[1]}, and all the improvements we made are served for our motivation.
